Gene network shaping of inherent noise spectra

D. W. Austin, M. S. Allen, J. M. McCollum, R. D. Dar, J. R. Wilgus, G. S. Sayler, N. F. Samatova, C. D. Cox and M. L. Simpson ()
Additional contact information
D. W. Austin: Molecular-Scale Engineering and Nanoscale Technologies Group
M. S. Allen: Molecular-Scale Engineering and Nanoscale Technologies Group
J. M. McCollum: University of Tennessee
R. D. Dar: Molecular-Scale Engineering and Nanoscale Technologies Group
J. R. Wilgus: University of Tennessee
G. S. Sayler: University of Tennessee
N. F. Samatova: Oak Ridge National Laboratory
C. D. Cox: University of Tennessee
M. L. Simpson: Molecular-Scale Engineering and Nanoscale Technologies Group

Nature, 2006, vol. 439, issue 7076, 608-611

Abstract: Abstract Recent work demonstrates that stochastic fluctuations in molecular populations have consequences for gene regulation1,2,3,4,5,6,7,8,9,10. Previous experiments focused on noise sources or noise propagation through gene networks by measuring noise magnitudes. However, in theoretical analysis, we showed that noise frequency content is determined by the underlying gene circuits, leading to a mapping between gene circuit structure and the noise frequency range11,12. An intriguing prediction from our previous studies was that negative autoregulation shifts noise to higher frequencies where it is more easily filtered out by gene networks11—a property that may contribute to the prevalence of autoregulation motifs (for example, found in the regulation of ∼40% of Escherichia coli genes). Here we measure noise frequency content in growing cultures of E. coli, and verify the link between gene circuit structure and noise spectra by demonstrating the negative autoregulation-mediated spectral shift. We further demonstrate that noise spectral measurements provide mechanistic insights into gene regulation, as perturbations of gene circuit parameters are discernible in the measured noise frequency ranges. These results suggest that noise spectral measurements could facilitate the discovery of novel regulatory relationships.

Date: 2006
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DOI: 10.1038/nature04194

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